A control model for human steering actions during lane change based on NGSIM dataset

Wang, Qi; Zhang, Jiajie; Li, Li; Hu, Jianming

doi:10.1109/itsc.2011.6082822

Cited by 1 publication

(3 citation statements)

References 27 publications

(14 reference statements)

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“…Recently, yet an alternative way to integrate both kinds of studies was proposed in [37], where we need both steering angle data and the corresponding trajectory. In current market vehicles, the steering angle can be measured by on-board sensors.…”

Section: Introductionmentioning

confidence: 99%

“…So, we directly estimate steering maneuvers from empirical DLC trajectories. However, the estimation algorithm proposed in [37] is time consuming and is also sensitive to measurement errors. This prevents us retrieving the common features of drivers from massive trajectory data.…”

Section: Introductionmentioning

confidence: 99%

“…This prevents us retrieving the common features of drivers from massive trajectory data. Nevertheless, the psychological origins of the steering control strategy were not analyzed in [37].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrieving Common Discretionary Lane Changing Characteristics From Trajectories

Cao

et al. 2018

IEEE Trans. Veh. Technol.

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Conventional lane change methods directly collected steering angle data via onboard sensors to accurately capture the actions of individual drivers. We can hardly use such methods to collect massive data from examinees, because of time and financial costs. In order to retrieve common steering behaviors for lots of drivers, we propose a method to retrieve common Discretionary Lane Change (DLC) steering characteristics from trajectory data. The key technique of this new method is solving an inverse problem that converts the measured trajectory into the unmeasured steering maneuvers under the assumed vehicle movement dynamics. We find that most normal DLC trajectories in the Next Generation Simulation (NGSIM) datasets could be well reproduced by a simple target heading angle preview control model. This finding sheds important light into driver behavior study and better explains how human control vehicles. Based on these findings, we can non-intrusively evaluate driving performance or physiological states of drivers based on online roadside monitoring data (e.g. the data collected from roadside video cameras). This opens a promising field of applications for enhancing driving safety.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%